Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 5 / 5 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 00:14 UTC — 21 Oct 2026 07:23 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 2 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 16 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 9 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 0 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2023 | All selected publications | 24 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 12 | 1 | 1/1 | 1/1 |
| 2025 | All selected publications | 1 | 2 | 2/2 | 2/2 |
| 2026 | All selected publications | 0 | 1 | 1/1 | 1/1 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 5 / 5 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T00:14:30.813181+00:00 — 2026-10-21T07:23:03.258818+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 5 | 5/5 |
| 2018 | All selected publications | 0 | 5 | 5/5 |
| 2019 | All selected publications | 0 | 5 | 5/5 |
| 2020 | All selected publications | 0 | 5 | 5/5 |
| 2021 | All selected publications | 0 | 5 | 5/5 |
| 2022 | All selected publications | 0 | 5 | 5/5 |
| 2023 | All selected publications | 0 | 5 | 5/5 |
| 2024 | All selected publications | 11 | 5 | 5/5 |
| 2025 | All selected publications | 18 | 5 | 5/5 |
| 2026 | All selected publications | 8 | 5 | 5/5 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2023 | 1 |
| 2024 | 1 |
| 2025 | 2 |
| 2026 | 1 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 2 | 0 | 1 | 2/2 | 0.0 | 0.0 | 0.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 25 | 36 | 3/3 | 8.33 | 9 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
With the intensity of the Fourth Industrial Revolution, Artificial Intelligence (AI) is being widely adopted to perform key tasks in economic activities. The audit profession has also embraced AI in the performance of its function in carrying out activities like audits, oversight, and advisory functions. The application of AI has been met with acceptance, given its advantages in some quarters and with resistance/scepticism in some. Proponents table benefits such as improved sampling procedures, reduced labour, and time in performing audits, increased efficiency, and effectiveness (due to increased audit coverage) including improved audit quality. Opponents raise pragmatic concerns such as the violation of ethical principles governing the audit profession, potential biases (loss of employment) as well as the challenges of coordinating machine and human activities. The study has two objectives. Firstly, to explore the role of AI in the external audit function. Secondly, to evaluate the ongoing debates on artificial intelligence and external auditing and assess the implications of using AI in the external audit function. The study adopts a qualitative research approach, employing a critical literature review. The study will accentuate the controversies and convergences among researchers on the role and implications of applying AI in external auditing to bring to light possible research gaps that can be explored by future researchers on the subject area. Highlighting the potential externalities of using AI has both theoretical and practical implications.
The purpose of this article is to clarify the impact of technological, organisational, and environmental contexts in which Artificial Intelligence solutions are implemented by the accounting and audit companies in Europe. The organizational and environmental contexts were not enough studied in accounting and audit field but are becoming more and more important in the future. The applied methodology was based on a structured interview, to which it has answered 62 top financial specialists from 18 European countries, in companies with more than 10 years of experience in the accounting and audit sector. To design the structured interview, it was used the Technology-Organisation-Environment framework. A serious concern for the companies’ representatives consists in the lack of specialists capable to understand and work with Artificial Intelligence solutions. One option that is generally preferred by companies is to prepare their employees for these new tasks, rather than hiring qualified persons. There are two methods that can be used when implementing Artificial Intelligence, to buy specific provided solutions from third parties, or to developed them internally. Data storage and security, the complexity of Artificial Intelligence solutions and government regulations do not represent a threat to companies willing to develop this area. The main contribution of this study consists of an extensive analysis of the most important elements of Technology-Organisational-Environmental framework and their applicability for accounting and audit companies, which implemented or are willing to implement Artificial Intelligence solutions.
This research highlights the perceptions of accountants and auditors in Romania, offering insights into how the use of Artificial Intelligence is impacting traditional financial reporting. This research addresses an important gap, respectively the lack of Artificial Intelligence in financial reporting in the Romanian context, as prior studies often focus on broader or economically advanced regions, such as more developed countries. The purpose of the study is to contribute to this emerging discussion, presented as a wide limitation of AI adoption in such a regulated sector. Insights were gathered through semi-structured interviews with pre-defined affirmations and open questions regarding the regulatory environment and how AI has been used to enhance financial reporting quality and accuracy but also how the regulatory environment limits the scope of AI. Additionally, a subset of international-level participants was interviewed for comparative purposes, enabling cross-border insights. Findings were further contextualized through the review of relevant literature to validate the comparative results by aligning them with perspectives with global trends. The key findings suggested companies often incorporate AI into their global strategies but fail to provide adequate training or support. Respondents also transparently communicated the lack of knowledge surrounding the subject given its benefits in the domain and lack of practical implications. Additionally, there is a clear need for a regulatory framework when applying AI in professions such as accounting.
Robotic Process Automation (RPA) is transforming knowledge-intensive professional services; nevertheless, data from consulting ecosystems in emerging markets is still scarce. This study investigates the organisational and human-centric consequences of RPA implementation in consulting businesses in Johannesburg, South Africa, concentrating on company performance, job security, employee performance, and customer engagement. We performed reliability testing, rotational factor analysis, Pearson correlations, and multiple linear regression in SAS using a cross-sectional survey of 256 consulting staff. The results indicate a favourable correlation between RPA and employee performance as well as customer interaction, however no statistically significant link was found for business performance or job security. The paper provides context-specific empirical evidence about RPA within the South African consulting ecosystem, indicating that early value realisation may manifest through enhancements in worker efficiency and client interactions rather than immediate increases in firm-level performance. The findings underscore the necessity for change management, reskilling, and appropriate automation governance to maintain human-technology complementarity.
The adoption of digital technologies in external auditing has become increasingly common and significant in the digital era, revolutionizing audit practices and offering several opportunities. This study explores whether the application of digital technologies in external auditing presents a double-edged sword, with the ability to transform and disrupt the audit profession. Through a comprehensive examination of the likely opportunities and challenges of the digital transformation of the external audit function, this study sought to provide an insightful discussion of the dual nature of the implications of digital technologies. The aim of this study is to not only provide a balanced assessment of the application of digital technologies in external auditing but also to recommend possible ways for auditors, auditee companies, and other stakeholders to reap maximum benefits from the use of digital technologies, including how these various stakeholders can navigate the associated challenges effectively. Opportunities stemming from the application of digital technologies include the enhancement of audit quality, improved accuracy in audit procedures, increased efficiency and effectiveness, heightened comprehensiveness and extensiveness in risk assessment, and an increased degree of confidence in the audited financial statements by stakeholders (reduction in the audit expectation gap). On the downside, challenges and risks concern the lack of or the need for new skills and competencies for auditors, technological complexities linked to digital technologies, cybersecurity risks, overdependence on technology affecting audit quality, audit evidence, professional skepticism, and the widening of the audit expectation gap. To maximize the possible benefits and minimize risks, this study recommends continuous professional development, capacity building through education, training and collaboration, better technology and data governance initiatives, and continuous assessment of risks.
Robotic Process Automation (RPA) is transforming knowledge-intensive professional services; nevertheless, data from consulting ecosystems in emerging markets is still scarce. This study investigates the organisational and human-centric consequences of RPA implementation in consulting businesses in Johannesburg, South Africa, concentrating on company performance, job security, employee performance, and customer engagement. We performed reliability testing, rotational factor analysis, Pearson correlations, and multiple linear regression in SAS using a cross-sectional survey of 256 consulting staff. The results indicate a favourable correlation between RPA and employee performance as well as customer interaction, however no statistically significant link was found for business performance or job security. The paper provides context-specific empirical evidence about RPA within the South African consulting ecosystem, indicating that early value realisation may manifest through enhancements in worker efficiency and client interactions rather than immediate increases in firm-level performance. The findings underscore the necessity for change management, reskilling, and appropriate automation governance to maintain human-technology complementarity.
This research highlights the perceptions of accountants and auditors in Romania, offering insights into how the use of Artificial Intelligence is impacting traditional financial reporting. This research addresses an important gap, respectively the lack of Artificial Intelligence in financial reporting in the Romanian context, as prior studies often focus on broader or economically advanced regions, such as more developed countries. The purpose of the study is to contribute to this emerging discussion, presented as a wide limitation of AI adoption in such a regulated sector. Insights were gathered through semi-structured interviews with pre-defined affirmations and open questions regarding the regulatory environment and how AI has been used to enhance financial reporting quality and accuracy but also how the regulatory environment limits the scope of AI. Additionally, a subset of international-level participants was interviewed for comparative purposes, enabling cross-border insights. Findings were further contextualized through the review of relevant literature to validate the comparative results by aligning them with perspectives with global trends. The key findings suggested companies often incorporate AI into their global strategies but fail to provide adequate training or support. Respondents also transparently communicated the lack of knowledge surrounding the subject given its benefits in the domain and lack of practical implications. Additionally, there is a clear need for a regulatory framework when applying AI in professions such as accounting.
The adoption of digital technologies in external auditing has become increasingly common and significant in the digital era, revolutionizing audit practices and offering several opportunities. This study explores whether the application of digital technologies in external auditing presents a double-edged sword, with the ability to transform and disrupt the audit profession. Through a comprehensive examination of the likely opportunities and challenges of the digital transformation of the external audit function, this study sought to provide an insightful discussion of the dual nature of the implications of digital technologies. The aim of this study is to not only provide a balanced assessment of the application of digital technologies in external auditing but also to recommend possible ways for auditors, auditee companies, and other stakeholders to reap maximum benefits from the use of digital technologies, including how these various stakeholders can navigate the associated challenges effectively. Opportunities stemming from the application of digital technologies include the enhancement of audit quality, improved accuracy in audit procedures, increased efficiency and effectiveness, heightened comprehensiveness and extensiveness in risk assessment, and an increased degree of confidence in the audited financial statements by stakeholders (reduction in the audit expectation gap). On the downside, challenges and risks concern the lack of or the need for new skills and competencies for auditors, technological complexities linked to digital technologies, cybersecurity risks, overdependence on technology affecting audit quality, audit evidence, professional skepticism, and the widening of the audit expectation gap. To maximize the possible benefits and minimize risks, this study recommends continuous professional development, capacity building through education, training and collaboration, better technology and data governance initiatives, and continuous assessment of risks.
The purpose of this article is to clarify the impact of technological, organisational, and environmental contexts in which Artificial Intelligence solutions are implemented by the accounting and audit companies in Europe. The organizational and environmental contexts were not enough studied in accounting and audit field but are becoming more and more important in the future. The applied methodology was based on a structured interview, to which it has answered 62 top financial specialists from 18 European countries, in companies with more than 10 years of experience in the accounting and audit sector. To design the structured interview, it was used the Technology-Organisation-Environment framework. A serious concern for the companies’ representatives consists in the lack of specialists capable to understand and work with Artificial Intelligence solutions. One option that is generally preferred by companies is to prepare their employees for these new tasks, rather than hiring qualified persons. There are two methods that can be used when implementing Artificial Intelligence, to buy specific provided solutions from third parties, or to developed them internally. Data storage and security, the complexity of Artificial Intelligence solutions and government regulations do not represent a threat to companies willing to develop this area. The main contribution of this study consists of an extensive analysis of the most important elements of Technology-Organisational-Environmental framework and their applicability for accounting and audit companies, which implemented or are willing to implement Artificial Intelligence solutions.
With the intensity of the Fourth Industrial Revolution, Artificial Intelligence (AI) is being widely adopted to perform key tasks in economic activities. The audit profession has also embraced AI in the performance of its function in carrying out activities like audits, oversight, and advisory functions. The application of AI has been met with acceptance, given its advantages in some quarters and with resistance/scepticism in some. Proponents table benefits such as improved sampling procedures, reduced labour, and time in performing audits, increased efficiency, and effectiveness (due to increased audit coverage) including improved audit quality. Opponents raise pragmatic concerns such as the violation of ethical principles governing the audit profession, potential biases (loss of employment) as well as the challenges of coordinating machine and human activities. The study has two objectives. Firstly, to explore the role of AI in the external audit function. Secondly, to evaluate the ongoing debates on artificial intelligence and external auditing and assess the implications of using AI in the external audit function. The study adopts a qualitative research approach, employing a critical literature review. The study will accentuate the controversies and convergences among researchers on the role and implications of applying AI in external auditing to bring to light possible research gaps that can be explored by future researchers on the subject area. Highlighting the potential externalities of using AI has both theoretical and practical implications.
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |
| ojs-rest | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 06:46 UTC |
| oai-pmh | 20 Oct 2026 19:55 UTC · ijrbs | complete · ijrbs · 20 Oct 2026 19:50 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |